1. Field of the Invention
The present invention relates to an expert system in which experiences and know-how of humans are reasoned or inferred by use of knowledge (a knowledge base) to solve a problem. Particularly, the present invention relates to a method of and a system for evaluating knowledge suitably applicable to a case where the knowledge is represented as a fuzzy knowledge for fuzzy inference or reasoning. Moreover, the present invention relates to a method of and a system for modifying or updating fuzzy knowledge based on an evaluation result as well as to a decision support system adopting a fuzzy knowledge which can be evaluated and modified.
2. Description of the Prior Art
Systems developed by applying fuzzy reasoning thereto have been described in articles such as "Fuzzy Control Method and Application of the Same to Real System", Transaction of the Institute of Electrical Engineers of Japan, Vol. 109-C, No. 5, pp. 330-336, May 1989 (Article 1); "Expert Shell for Control of Plant (ERIC)", Automation, Vol. 33, No. 6, pp. 17-21, June 1988 (Article 2); "Expert System for Investment based on Fuzzy Reasoning", Journal of Information Processing Society of Japan, pp. 963-969, Aug. 1989 (Article 3); and "Fuzzy Expert System Building Shell", Journal of Information Processing Society of Japan, pp. 948-956, August 1989 (Article 4).
In accordance with a system described in Article 1, know-how of experts and/or specialists is collected as fuzzy rules or knowledge rules in a knowledge rule base such that depending on a state of a reasoning object, a reasoning operation is conducted by use of contents of the knowledge rule base. In an automatic train operation system described in Article 1, characteristics of an object (a train) of reasoning are kept unchanged. Consequently, once the know-how of experts (drivers) is gathered as rules in a knowledge rule base, the system is operated for a long period of time with the knowledge rule base.
Article 2 also describes a fuzzy rule representation and a fuzzy inference mechanism. In addition, fuzzy variables have been described. More specifically, it has been notified that when setting fuzzy variables, a trial-and-error procedure is required to be repeatedly achieved to adjust membership functions of fuzzy variables, thereby attaining the membership functions appropriately adaptive to the situation. Moreover, there has been described that fuzzy variables can be additionally set when necessary.
Article 3 describes a method of registering and of modifying rules, which is devised on recognition that even when a rule leads to a satisfactory result with respect to data in the past, it is not guaranteed that the rule is also efficient in the future. In this method, data attained in a fixed period of time in the past are processed to obtain parameter values such that based on the resultant parameter values, the rule registration and modification are accomplished. However, only a variable can be set as an object of the learning; furthermore, the variable is required to be contained in an if clause of a statement of if - - -, then - - -.
Article 4 describes a method of generating and of evaluating fuzzy control rules through a simulation. In this method, more specifically, by checking such items displayed by a graph representation function as input and output values at respective points of time in a simulation and membership functions associated with output values resultant from a fuzzy reasoning, the user can confirm effectiveness of fuzzy control rules for modifications thereof.